ValueError:维度“str”太多

Question

我有以下问题。我有一个类似这样的输入：x = [1,2,3,4,5,6,7,8,9,10]现在我希望将字母作为输入，而不是数字。它应该是这样的：x = ['a', 'b', 'c', 'd', 'e', ​​'f', 'g', 'h', 'i', 'j']将出现以下错误消息：

input = var (torch.Tensor ([x for _ in range (30)])) ValueError: too many dimensions 'str'.

如何使用字母作为输入？当前代码如下所示：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable as var
import torch.optim as optim

class Nnetz(nn.Module):
    def __init__(self):
        super(Nnetz, self).__init__()
        self.lin1 = nn.Linear(10, 10)
        self.lin2 = nn.Linear(10, 10)
        self.lin3 = nn.Linear(10, 10)
        self.lin4 = nn.Linear(10, 10)
        self.lin5 = nn.Linear(10, 10)
        self.lin6 = nn.Linear(10, 10)
        
    
    def forward(self, x):
        x = F.relu(self.lin1(x))
        x = self.lin2(x)
        x = self.lin3(x)
        x = self.lin4(x)
        x = self.lin5(x)
        x = self.lin6(x)
        return x
    
    def num_flat_features(self, x):
        size = x.size()[1:]
        num = 1
        for i in size:
            num *= i
        return num
        
    
netz = Nnetz()

for i in range(100):
    x = [10,9,8,7,6,5,4,3,2,1,]
    input = var(torch.Tensor([x for _ in range(10)]))
    out = netz(input)

    x = [1,2,3,4,5,6,7,8,9,10]
    target = var(torch.Tensor([x for _ in range(10)]))
    criterion = nn.MSELoss()
    loss = criterion(out, target)
    print(loss)

    netz.zero_grad()
    loss.backward()
    optimizer = optim.SGD(netz.parameters(), lr=0.01)
    optimizer.step()